Industries

AI applied to real business problems

The same rigorous toolkit, focused where it counts. Each use case below links back to the capabilities that power it.

Finance

Details

From real-time fraud detection to grounded analyst research, we bring defensible, well-evaluated models to high-stakes financial decisions.

Real-time fraud & transaction anomaly detection

Balanced ensemble models that catch fraud while keeping false declines low.

Analyst research assistants over filings & reports

RAG systems that answer cross-sector questions with page-level citations.

Credit risk & default scoring

Validated, explainable scoring models served behind production APIs.

Market & operational anomaly monitoring

Statistical and ML monitoring that surfaces what changed and why.

Medicine & Healthcare

Details

Diagnostic-grade computer vision and clinical knowledge assistants, designed for high recall and built to be interpreted, audited and trusted.

Medical image classification & triage

MRI, X-ray and pathology classifiers tuned to minimize missed findings.

Explainable diagnostic support

Attention heatmaps so clinicians see what drove each prediction.

Clinical literature & guideline Q&A

Assistants grounded in your protocols and the latest evidence, with sources.

Patient risk stratification

Predictive models that flag high-risk patients earlier in the pathway.

Manufacturing

Details

Vision-based quality control, predictive maintenance and operational analytics that reduce downtime, scrap and disruption.

Automated visual defect & quality inspection

CNN inspection that catches defects faster and more consistently than manual QA.

Predictive maintenance & failure forecasting

Anomaly and time-aware models that predict failures before they stop the line.

Operational disruption & throughput analytics

Statistical analysis that isolates the real drivers of delay and loss.

Supply-chain network optimization

Graph models that reveal hidden dependencies and bottlenecks across suppliers.

Cybersecurity

Details

Threat classification, behavioral anomaly detection and graph-based intrusion analysis — augmented by AI assistants for the SOC.

Threat & malicious-content classification

Text and behavioral classifiers that separate threats from noise at scale.

Intrusion & lateral-movement detection

Graph neural networks that trace attack paths across hosts and identities.

Log & behavioral anomaly detection

Unsupervised models that flag the abnormal without endless rules.

AI-assisted SOC & threat-intel summarization

RAG assistants that summarize alerts and intel with linked evidence.

IT Automation

Details

RAG-powered assistants, agentic workflows and production MLOps that take repetitive IT and knowledge work off your team's plate.

Internal knowledge assistants & runbooks

Grounded Q&A over your docs, wikis and runbooks — answers with sources.

Agentic workflow automation

LLM agents that triage, route and resolve repetitive multi-step tasks.

Intelligent document processing & ticket triage

Classification and extraction that route work to the right place automatically.

ML platform & deployment engineering

The pipelines, APIs and containers that keep models running in production.

Don't see your exact problem?

These are starting points, not a menu. Tell us your decision and we'll scope an approach.

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